Tag Archives: big data

The Emergence of the Third Platform

By Andras Szakal, Vice President and Chief Technology Officer, IBM U.S. Federal

By 2015 there will be more than 5.6 billion personal devices in use around the world. Personal mobile computing, business systems, e-commerce, smart devices and social media are generating an astounding 2.5 billion gigabytes of data per day. Non-mobile network enabled intelligent devices, often referred to as the Internet of Things (IoT), is poised to explode to over 1 trillion devices by 2015.

Rapid innovation and astounding growth in smart devices is driving new business opportunities and enterprise solutions. Many of these new opportunities and solutions are based on deep insight gained through analysis of the vast amount of data being generated.

The expansive growth of personal and pervasive computing power continues to drive innovation that is giving rise to a new class of systems and a pivot to a new generation of computing platform. Over the last fifty years, two generations of computing platform have dominated the business and consumer landscape. The first generation was dominated by the monolithic mainframe, while distributed computing and the Internet characterized the second generation. Cloud computing, Big Data/Analytics, the Internet of Things (IoT), mobile computing and even social media are the core disruptive technologies that are now converging at the cross roads of the emergence of a third generation of computing platform.

This will require new approaches to enterprise and business integration and interoperability. Industry bodies like The Open Group must help guide customers through the transition by facilitating customer requirements, documenting best practices, establishing integration standards and transforming the current approach to Enterprise Architecture, to adapt to the change in which organizations will build, use and deploy the emerging third generation of computing platform.

Enterprise Computing Platforms

An enterprise computing platform provides the underlying infrastructure and operating environment necessary to support business interactions. Enterprise systems are often comprised of complex application interactions necessary to support business processes, customer interactions, and partner integration. These interactions coupled with the underlying operating environment define an enterprise systems architecture.

The hallmark of successful enterprise systems architecture is a standardized and stable systems platform. This is an underlying operating environment that is stable, supports interoperability, and is based on repeatable patterns.

Enterprise platforms have evolved from the monolithic mainframes of the 1960s and 1970s through the advent of the distributed systems in the 1980s. The mainframe-based architecture represented the first true enterprise operating platform, referred to henceforth as the First Platform. The middleware-based distributed systems that followed and ushered in the dawn of the Internet represented the second iteration of platform architecture, referred to as the Second Platform.

While the creation of the Internet and the advent of web-based e-commerce are of historical significance, the underlying platform was still predominantly based on distributed architectures and therefore is not recognized as a distinct change in platform architecture. However, Internet-based e-commerce and service-based computing considerably contributed to the evolution toward the next distinct version of the enterprise platform. This Third Platform will support the next iteration of enterprise systems, which will be born out of multiple simultaneous and less obvious disruptive technology shifts.

The Convergence of Disruptive Technologies

The emergence of the third generation of enterprise platforms is manifested at the crossroads of four distinct, almost simultaneous, disruptive technology shifts; cloud computing, mobile computing, big data-based analytics and the IoT. The use of applications based on these technologies, such as social media and business-driven insight systems, have contributed to both the convergence and rate of adoption.

These technologies are dramatically changing how enterprise systems are architected, how customers interact with business, and the rate and pace of development and deployment across the enterprise. This is forcing vendors, businesses, and governments to shift their systems architectures to accommodate integrated services that leverage cloud infrastructure, while integrating mobile solutions and supporting the analysis of the vast amount of data being generated by mobile solutions and social media. All this is happening while maintaining the integrity of the evolving businesses capabilities, processes, and transactions that require integration with business systems such as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM).

Cloud computing and the continued commoditization of computer storage are key facilitating elements of this convergence. Cloud computing lowers the complexity of enterprise computing through virtualization and automated infrastructure provisioning, while solid-state and software-based Internet storage has made big data practical and affordable. Cloud computing solutions continue to evolve and offer innovative services like Platform as a Service (PaaS)-based development environments that integrate directly with big data solutions. Higher density, cloud-based and solid-state storage continue to lower the cost and complexity of storage and big data solutions.

The emergence of the smartphone and enterprise mobile computing is a key impetus for the emergence of big data solutions and an explosion of innovative storage technologies. The modern mobile platform, with all its rich applications, device sensors, and access to social networks, is almost single-handedly responsible for the explosion of data and the resulting rush to provide solutions to analyze and act on the insight contained in the vast ocean of personalized information. In turn, this phenomenon has created a big data market ecosystem based on the premise that open data is the new natural resource.

The emergence of sensor-enabled smartphones has foreshadowed the potential value of making everyday devices interconnected and intelligent by adding network-based sensors that allow devices to enhance their performance by interacting with their environment, and through collaboration with other devices and enterprise systems in the IoT. For example, equipment manufacturers are using sensors to gain insight into the condition of fielded equipment. This approach reduces both the mean time to failure and pinpoints manufacturing quality issues and potential design flaws. This system of sensors also integrates with the manufacturer’s internal supply chain systems to identify needed parts, and optimizes the distribution process. In turn, the customer benefits by avoiding equipment downtime through scheduling maintenance before a part fails.

Over time, the IoT will require an operating environment for devices that integrates with existing enterprise business systems. But this will require that smart devices effectively integrate with cloud-based enterprise business systems, the enterprise customer engagement systems, as well as the underlying big data infrastructure responsible for gleaning insight into the data this vast network of sensors will generate. While each of these disruptive technology shifts has evolved separately, they share a natural affinity for interaction, collaboration, and enterprise integration that can be used to optimize an enterprise’s business processes.

Evolving Enterprise Business Systems

Existing enterprise systems (ERP, CRM, Supply Chain, Logistics, etc.) are still essential to the foundation of a business or government and form Systems of Record (SoR) that embody core business capabilities and the authoritative processes based on master data records. The characteristics of SoR are:

  • Encompass core business functions
  • Transactional in nature
  • Based on structured databases
  • Authoritative source of information (master data records)
  • Access is regulated
  • Changes follow a rigorous governance process.

Mobile systems, social media platforms, and Enterprise Market Management (EMM) solutions form another class of systems called Systems of Engagement (SoE). Their characteristics are:

  • Interact with end-users through open collaborative interfaces (mobile, social media, etc.)
  • High percentage of unstructured information
  • Personalized to end-user preferences
  • Context-based analytical business rules and processing
  • Access is open and collaborative
  • Evolves quickly and according to the needs of the users.

The emergence of the IoT is embodied in a new class of system, Systems of Sensors (SoS), which includes pervasive computing and control. Their characteristics are:

  • Based on autonomous network-enabled devices
  • Devices that use sensors to collect information about the environment
  • Interconnected with other devices or enterprise engagement systems
  • Changing behavior based on intelligent algorithms and environmental feedback
  • Developed through formal product engineering process
  • Updates to device firmware follow a continuous lifecycle.

The Third Platform

The Third Platform is a convergence of cloud computing, big data solutions, mobile systems and the IoT integrated into the existing enterprise business systems.

The Three Classes of System

Figure 1: The Three Classes of Systems within the Third Platform

The successful implementation and deployment of enterprise SoR has been embodied in best practices, methods, frameworks, and techniques that have been distilled into enterprise architecture. The same level of rigor and pattern-based best practices will be required to ensure the success of solutions based on Third Platform technologies. Enterprise architecture methods and models need to evolve to include guidance, governance, and design patterns for implementing business solutions that span the different classes of system.

The Third Platform builds upon many of the concepts that originated with Service-Oriented Architecture (SOA) and dominated the closing stanza of the period dominated by the Second Platform technologies. The rise of the Third Platform provides the technology and environment to enable greater maturity of service integration within an enterprise.

The Open Group Service Integration Maturity Model (OSIMM) standard[1] provides a way in which an organization can assess its level of service integration maturity. Adoption of the Third Platform inherently addresses many of the attributes necessary to achieve the highest levels of service integration maturity defined by OSIMM. It will enable new types of application architecture that can support dynamically reconfigurable business and infrastructure services across a wide variety of devices (SoS), internal systems (SoR), and user engagement platforms (SoE).

Solution Development

These new architectures and the underlying technologies will require adjustments to how organizations approach enterprise IT governance, to lower the barrier of entry necessary to implement and integrate the technologies. Current adoption requires extensive expertise to implement, integrate, deploy, and maintain the systems. First market movers have shown the rest of the industry the realm of the possible, and have reaped the rewards of the early adopter.

The influence of cloud and mobile-based technologies has changed the way in which solutions will be developed, delivered, and maintained. SoE-based solutions interact directly with customers and business partners, which necessitates a continuous delivery of content and function to align with the enterprise business strategy.

Most cloud-based services employ a roll-forward test and delivery model. A roll-forward model allows an organization to address functional inadequacies and defects in almost real-time, with minimal service interruptions. The integration and automation of development and deployment tools and processes reduces the risk of human error and increases visibility into quality. In many cases, end-users are not even aware of updates and patch deployments.

This new approach to development and operations deployment and maintenance is referred to as DevOps – which combines development and operations tools, governance, and techniques into a single tool set and management practice. This allows the business to dictate, not only the requirements, but also the rate and pace of change aligned to the needs of the enterprise.

[1] The Open Group Service Integration Maturity Model (OSIMM), Open Group Standard (C117), published by The Open Group, November 2011; refer to: www.opengroup.org/bookstore/catalog/c117.htm

Andras2

Figure 2: DevOps: The Third Platform Solution Lifecycle

The characteristics of an agile DevOps approach are:

  • Harmonization of resources and practices between development and IT operations
  • Automation and integration of the development and deployment processes
  • Alignment of governance practices to holistically address development and operations with business needs
  • Optimization of the DevOps process through continuous feedback and metrics.

In contrast to SoE, SoR have a slower velocity of delivery. Such systems are typically released on fixed, pre-planned release schedules. Their inherent stability of features and capabilities necessitates a more structured and formal development approach, which traditionally equates to fewer releases over time. Furthermore, the impact changes to SoR have on core business functionality limits the magnitude and rate of change an organization is able to tolerate. But the emergence of the Third Platform will continue to put pressure on these core business systems to become more agile and flexible in order to adapt to the magnitude of events and information generated by mobile computing and the IoT.

As the technologies of the Third Platform coalesce, organizations will need to adopt hybrid development and delivery models based on agile DevOps techniques that are tuned appropriately to the class of system (SoR, SoS or SoS) and aligned with an acceptable rate of change.

DevOps is a key attribute of the Third Platform that will shift the fundamental management structure of the IT department. The Third Platform will usher in an era where one monolithic IT department is no longer necessary or even feasible. The line between business function and IT delivery will be imperceptible as this new platform evolves. The lines of business will become intertwined with the enterprise IT functions, ultimately leading to the IT department and business capability becoming synonymous. The recent emergence of the Enterprise Market Management organizations is an example where the marketing capabilities and the IT delivery systems are managed by a single executive – the Enterprise Marketing Officer.

The Challenge

The emergence of a new enterprise computing platform will usher in opportunity and challenge for businesses and governments that have invested in the previous generation of computing platforms. Organizations will be required to invest in both expertise and technologies to adopt the Third Platform. Vendors are already offering cloud-based Platform as a Service (PaaS) solutions that will provide integrated support for developing applications across the three evolving classes of systems – SoS, SoR, and SoE. These new development platforms will continue to evolve and give rise to new application architectures that were unfathomable just a few years ago. The emergence of the Third Platform is sure to spawn an entirely new class of dynamically reconfigurable intelligent applications and devices where applications reprogram their behavior based on the dynamics of their environment.

Almost certainly this shift will result in infrastructure and analytical capacity that will facilitate the emergence of cognitive computing which, in turn, will automate the very process of deep analysis and, ultimately, evolve the enterprise platform into the next generation of computing. This shift will require new approaches, standards and techniques for ensuring the integrity of an organization’s business architecture, enterprise architecture and IT systems architectures.

To effectively embrace the Third Platform, organizations will need to ensure that they have the capability to deliver boundaryless systems though integrated services that are comprised of components that span the three classes of systems. This is where communities like The Open Group can help to document architectural patterns that support agile DevOps principles and tooling as the Third Platform evolves.

Technical standardization of the Third Platform has only just begun; for example, standardization of the cloud infrastructure has only recently crystalized around OpenStack. Mobile computing platform standardization remains fragmented across many vendor offerings even with the support of rigid developer ecosystems and open sourced runtime environments. The standardization and enterprise support for SoS is still nascent but underway within groups like the Allseen Alliance and with the Open Group’s QLM workgroup.

Call to Action

The rate and pace of innovation, standardization, and adoption of Third Platform technologies is astonishing but needs the guidance and input from the practitioner community. It is incumbent upon industry communities like the Open Group to address the gaps between traditional Enterprise Architecture and an approach that scales to the Internet timescales being imposed by the adoption of the Third Platform.

The question is not whether Third Platform technologies will dominate the IT landscape, but rather how quickly this pivot will occur. Along the way, the industry must apply the open standards processes to ensure against the fragmentation into multiple incompatible technology platforms.

The Open Group has launched a new forum to address these issues. The Open Group Open Platform 3.0™ Forum is intended to provide a vendor-neutral environment where members share knowledge and collaborate to develop standards and best practices necessary to help guide the evolution of Third Platform technologies and solutions. The Open Platform 3.0 Forum will provide a place where organizations can help illuminate their challenges in adopting Third Platform technologies. The Open Platform 3.0 Forum will help coordinate standards activities that span existing Open Group Forums and ensure a coordinated approach to Third Platform standardization and development of best practices.

Innovation itself is not enough to ensure the value and viability of the emerging platform. The Open Group can play a unique role through its focus on Boundaryless Information Flow™ to facilitate the creation of best practices and integration techniques across the layers of the platform architecture.

andras-szakalAndras Szakal, VP and CTO, IBM U.S. Federal, is responsible for IBM’s industry solution technology strategy in support of the U.S. Federal customer. Andras was appointed IBM Distinguished Engineer and Director of IBM’s Federal Software Architecture team in 2005. He is an Open Group Distinguished Certified IT Architect, IBM Certified SOA Solution Designer and a Certified Secure Software Lifecycle Professional (CSSLP).  Andras holds undergraduate degrees in Biology and Computer Science and a Masters Degree in Computer Science from James Madison University. He has been a driving force behind IBM’s adoption of government IT standards as a member of the IBM Software Group Government Standards Strategy Team and the IBM Corporate Security Executive Board focused on secure development and cybersecurity. Andras represents the IBM Software Group on the Board of Directors of The Open Group and currently holds the Chair of The Open Group Certified Architect (Open CA) Work Group. More recently, he was appointed chair of The Open Group Trusted Technology Forum and leads the development of The Open Trusted Technology Provider Framework.

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IT Trends Empowering Your Business is Focus of The Open Group London 2014

By The Open Group

The Open Group, the vendor-neutral IT consortium, is hosting an event in London October 20th-23rd at the Central Hall, Westminster. The theme of this year’s event is on how new IT trends are empowering improvements in business and facilitating enterprise transformation.

Objectives of this year’s event:

  • Show the need for Boundaryless Information Flow™, which would result in more interoperable, real-time business processes throughout all business ecosystems
  • Examine the use of developing technology such as Big Data and advanced data analytics in the financial services sector: to minimize risk, provide more customer-centric products and identify new market opportunities
  • Provide a high-level view of the Healthcare ecosystem that identifies entities and stakeholders which must collaborate to enable the vision of Boundaryless Information Flow
  • Detail how the growth of “The Internet of Things” with online currencies and mobile-enabled transactions has changed the face of financial services, and poses new threats and opportunities
  • Outline some of the technological imperatives for Healthcare providers, with the use of The Open Group Open Platform 3.0™ tools to enable products and services to work together and deploy emerging technologies freely and in combination
  • Describe how to develop better interoperability and communication across organizational boundaries and pursue global standards for Enterprise Architecture for all industries

Key speakers at the event include:

  • Allen Brown, President & CEO, The Open Group
  • Magnus Lindkvist, Futurologist
  • Hans van Kesteren, VP & CIO Global Functions, Shell International, The Netherlands
  • Daniel Benton, Global Managing Director, IT Strategy, Accenture

Registration for The Open Group London 2014 is open and available to members and non-members. Please register here.

Join the conversation via Twitter – @theopengroup #ogLON

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Business Benefit from Public Data

By Dr. Chris Harding, Director for Interoperability, The Open Group

Public bodies worldwide are making a wealth of information available, and encouraging its commercial exploitation. This sounds like a bonanza for the private sector at the public expense, but entrepreneurs are holding back. A healthy market for products and services that use public-sector information would provide real benefits for everyone. What can we do to bring it about?

Why Governments Give Away Data

The EU directive of 2003 on the reuse of public sector information encourages the Member States to make as much information available for reuse as possible. This directive was revised and strengthened in 2013. The U.S. Open Government Directive of 2009 provides similar encouragement, requiring US government agencies to post at least three high-value data sets online and register them on its data.gov portal. Other countries have taken similar measures to make public data publicly available.

Why are governments doing this? There are two main reasons.

One is that it improves the societies that they serve and the governments themselves. Free availability of information about society and government makes people more effective citizens and makes government more efficient. It illuminates discussion of civic issues, and points a searchlight at corruption.

The second reason is that it has a positive effect on the wealth of nations and their citizens. The EU directive highlights the ability of European companies to exploit the potential of public-sector information, and contribute to economic growth and job creation. Information is not just the currency of democracy. It is also the lubricant of a successful economy.

Success Stories

There are some big success stories.

If you drive a car, you probably use satellite navigation to find your way about, and this may use public-sector information. In the UK, for example, map data that can be used by sat-nav systems is supplied for commercial use by a government agency, the Ordnance Survey.

When you order something over the web for delivery to your house, you often enter a postal code and see most of the address auto-completed by the website. Postcode databases are maintained by national postal authorities, which are generally either government departments or regulated private corporations, and made available by them for commercial use. Here, the information is not directly supporting a market, but is contributing to the sale of a range of unrelated products and services.

The data may not be free. There are commercial arrangements for supply of map and postcode data. But it is available, and is the basis for profitable products and for features that make products more competitive.

The Bonanza that Isn’t

These successes are, so far, few in number. The economic benefits of open government data could be huge. The McKinsey Global Institute estimates a potential of between 3 and 5 trillion dollars annually. Yet the direct impact of Open Data on the EU economy in 2010, seven years after the directive was issued, is estimated by Capgemini at only about 1% of that, although the EU accounts for nearly a quarter of world GDP.

The business benefits to be gained from using map and postcode data are obvious. There are other kinds of public sector data, where the business benefits may be substantial, but they are not easy to see. For example, data is or could be available about public transport schedules and availability, about population densities, characteristics and trends, and about real estate and land use. These are all areas that support substantial business activity, but businesses in these areas seldom make use of public sector information today.

Where are the Products?

Why are entrepreneurs not creating these potentially profitable products and services? There is one obvious reason. The data they are interested in is not always available and, where it is available, it is provided in different ways, and comes in different formats. Instead of a single large market, the entrepreneur sees a number of small markets, none of which is worth tackling. For example, the market for an application that plans public transport journeys across a single town is not big enough to justify substantial investment in product development. An application that could plan journeys across any town in Europe would certainly be worthwhile, but is not possible unless all the towns make this data available in a common format.

Public sector information providers often do not know what value their data has, or understand its applications. Working within tight budgets, they cannot afford to spend large amounts of effort on assembling and publishing data that will not be used. They follow the directives but, without common guidelines, they simply publish whatever is readily to hand, in whatever form it happens to be.

The data that could support viable products is not available everywhere and, where it is available, it comes in different formats. (One that is often used is PDF, which is particularly difficult to process as an information source.) The result is that the cost of product development is high, and the expected return is low.

Where is the Market?

There is a second reason why entrepreneurs hesitate. The shape of the market is unclear. In a mature market, everyone knows who the key players are, understands their motivations, and can predict to some extent how they will behave. The market for products and services based on public sector information is still taking shape. No one is even sure what kinds of organization will take part, or what they will do. How far, for example, will public-sector bodies go in providing free applications? Can large corporations buy future dominance with loss-leader products? Will some unknown company become an overnight success, like Facebook? With these unknowns, the risks are very high.

Finding the Answers

Public sector information providers and standards bodies are tackling these problems. The Open Group participates in SHARE-PSI, the European network for the exchange of experience and ideas around implementing open data policies in the public sector. The experience gained by SHARE-PSI will be used by the World-Wide Web Consortium as a basis for standards and guidelines for publication of public sector information. These standards and guidelines may be used, not just by the public sector, but by not-for-profit bodies and even commercial corporations, many of which have information that they want to make freely available.

The Open Group is making a key contribution by helping to map the shape of the market. It is using the Business Scenario technique from its well-known Enterprise Architecture methodology TOGAF® to identify the kinds of organization that will take part, and their objectives and concerns.

There will be a preview of this on October 22 at The Open Group event in London which will feature a workshop session on Open Public Sector Data. This workshop will look at how Open Data can help business, present a draft of the Business Scenario, and take input from participants to help develop its conclusions.

The developed Business Scenario will be presented at the SHARE-PSI workshop in Lisbon on December 3-4. The theme of this workshop is encouraging open data usage by commercial developers. It will bring a wide variety of stakeholders together to discuss and build the relationship between the public and private sectors. It will also address, through collaboration with the EU LAPSI project, the legal framework for use of open public sector data.

Benefit from Participation!

If you are thinking about publishing or using public-sector data, you can benefit from these workshops by gaining an insight into the way that the market is developing. In the long term, you can influence the common standards and guidelines that are being developed. In the short term, you can find out what is happening and network with others who are interested.

The social and commercial benefits of open public-sector data are not being realized today. They can be realized through a healthy market in products and services that process the data and make it useful to citizens. That market will emerge when public bodies and businesses clearly understand the roles that they can play. Now is the time to develop that understanding and begin to profit from it.

Register for The Open Group London 2014 event at http://www.opengroup.org/london2014/registration.

Find out how to participate in the Lisbon SHARE-PSI workshop at http://www.w3.org/2013/share-psi/workshop/lisbon/#Participation

 

Chris HardingDr. Chris Harding is Director for Interoperability at The Open Group. He has been with The Open Group for more than ten years, and is currently responsible for managing and supporting its work on interoperability, including SOA and interoperability aspects of Cloud Computing, and the Open Platform 3.0™ Forum. He is a member of the BCS, the IEEE and the AEA, and is a certified TOGAF® practitioner.

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The Open Group London 2014: Open Platform 3.0™ Panel Preview with Capgemini’s Ron Tolido

By The Open Group

The third wave of platform technologies is poised to revolutionize how companies do business not only for the next few years but for years to come. At The Open Group London event in October, Open Group CTO Dave Lounsbury will be hosting a panel discussion on how The Open Group Open Platform 3.0™ will affect Enterprise Architectures. Panel speakers include IBM Vice President and CTO of U.S. Federal IMT Andras Szakal and Capgemini Senior Vice President and CTO for Application Services Ron Tolido.

We spoke with Tolido in advance of the event about the progress companies are making in implementing third platform technologies, the challenges facing the industry as Open Platform 3.0 evolves and the call to action he envisions for The Open Group as these technologies take hold in the marketplace.

Below is a transcript of that conversation.

From my perspective, we have to realize: What is the call to action that we should have for ourselves? If we look at the mission of Boundaryless Information Flow™ and the need for open standards to accommodate that, what exactly can The Open Group and any general open standards do to facilitate this next wave in IT? I think it’s nothing less than a revolution. The first platform was the mainframe, the second platform was the PC and now the third platform is anything beyond the PC, so all sorts of different devices, sensors and ways to access information, to deploy solutions and to connect. What does it mean in terms of Boundaryless Information Flow and what is the role of open standards to make that platform succeed and help companies to thrive in such a new world?

That’s the type of call to action I’m envisioning. And I believe there are very few Forums or Work Groups within The Open Group that are not affected by this notion of the third platform. Firstly, I believe an important part of the Open Platform 3.0 Forum’s mission will be to analyze, to understand, the impacts of the third platform, of all those different areas that we’re evolving currently in The Open Group, and, if you like, orchestrate them a bit or be a catalyst in all the working groups and forums.

In a blog you wrote this summer for Capgemini’s CTO Blog you cited third platform technologies as being responsible for a renewed interest in IT as an enabler of business growth. What is it about the Third Platform is driving that interest?

It’s the same type of revolution as we’ve seen with the PC, which was the second platform. A lot of people in business units—through the PC and client/server technologies and Windows and all of these different things—realized that they could create solutions of a whole new order. The second platform meant many more applications, many more uses, much more business value to be achieved and less direct dependence on the central IT department. I think we’re seeing a very similar evolution right now, but the essence of the move is not that it moves us even further away from central IT but it puts the power of technology right in the business. It’s much easier to create solutions. Nowadays, there are many more channels that are so close in business that it takes business people to understand them. This explains also why business people like the third platform so much—it’s the Cloud, it’s mobile, social, it’s big data, all of these are waves that bring technology closer to the business, and are easy to use with very apparent business value that haven’t seen before, certainly not in the PC era. So we’re seeing a next wave, almost a revolution in terms of how easy it is to create solutions and how widely spread these solutions can be. Because again, as with the PC, it’s many more applications yet again and many more potential uses that can be connected through these applications, so that’s the very nature of the revolution and that also explains why business people like the third platform so much. So what people say to me these days on the business side is ‘We love IT, it’s just these bloody IT people that are the problem.’

Due to the complexities of building the next wave of platform computing, do you think that we may hit a point of fatigue as companies begin to tackle everything that is involved in creating that platform and making it work together?

The way I see it, that’s still the work of the IT community and the Enterprise Architect and the platform designer. It’s the very nature of the platform is that it’s attractive to use it, not to build it. The very nature of the platform is to connect to it and launch from it, but building the platform is an entirely different story. I think it requires platform designers and Enterprise Architects, if you like, and people to do the plumbing and do the architecting and the design underneath. But the real nature of the platform is to use it and to build upon it rather than to create it. So the happy view is that the “business people” don’t have to construct this.

I do believe, by the way, that many of the people in The Open Group will be on the side of the builders. They’re supposed to like complexity and like reducing it, so if we do it right the users of the platform will not notice this effort. It’s the same with the Cloud—the problem with the Cloud nowadays is that many people are tempted to run their own clouds, their own technologies, and before they know it, they only have additional complexity on their agenda, rather than reduced, because of the Cloud. It’s the same with the third platform—it’s a foundation which is almost a no-brainer to do business upon, for the next generation of business models. But if we do it wrong, we only have additional complexity on our hands, and we give IT a bad name yet again. We don’t want to do that.

What are Capgemini customers struggling with the most in terms of adopting these new technologies and putting together an Open Platform 3.0?

What you currently see—and it’s not always good to look at history—but if you look at the emergence of the second platform, the PC, of course there were years in which central IT said ‘nobody needs a PC, we can do it all on the mainframe,’ and they just didn’t believe it and business people just started to do it themselves. And for years, we created a mess as a result of it, and we’re still picking up some of the pieces of that situation. The question for IT people, in particular, is to understand how to find this new rhythm, how to adopt the dynamics of this third platform while dealing with all the complexity of the legacy platform that’s already there. I think if we are able to accelerate creating such a platform—and I think The Open Group will be very critical there—what exactly should be in the third platform, what type of services should you be developing, how would these services interact, could we create some set of open standards that the industry could align to so that we don’t have to do too much work in integrating all that stuff. If we, as The Open Group, can create that industry momentum, that, at least, would narrow the gap between business and IT that we currently see. Right now IT’s very clearly not able to deliver on the promise because they have their hands full with surviving the existing IT landscape, so unless they do something about simplifying it on the one hand and bridging that old world with the new one, they might still be very unpopular in the forthcoming years. That’s not what you want as an IT person—you want to enable business and new business. But I don’t think we’ve been very effective with that for the past ten years as an industry in general, so that’s a big thing that we have to deal with, bridging the old world with the new world. But anything we can do to accelerate and simplify that job from The Open Group would be great, and I think that’s the very essence of where our actions would be.

What are some of the things that The Open Group, in particular, can do to help affect these changes?

To me it’s still in the evangelization phase. Sooner or later people have to buy it and say ‘We get it, we want it, give me access to the third platform.’ Then the question will be how to accelerate building such an actual platform. So the big question is: What does such a platform look like? What types of services would you find on such a platform? For example, mobility services, data services, integration services, management services, development services, all of that. What would that look like in a typical Platform 3.0? Maybe even define a catalog of services that you would find in the platform. Then, of course, if you could use such a catalog or shopping list, if you like, to reach out to the technology suppliers of this world and convince them to pick that up and gear around these definitions—that would facilitate such a platform. Also maybe the architectural roadmap—so what would an architecture look like and what would be the typical five ways of getting there? We have to start with your local situation, so probably also several design cases would be helpful, so there’s an architectural dimension here.

Also, in terms of competencies, what type of competencies will we need in the near future to be able to supply these types of services to the business? That’s, again, very new—in this case, IT Specialist Certification and Architect Certification. These groups also need to think about what are the new competencies inherent in the third platform and how does it affect things like certification criteria and competency profiles?

In other areas, if you look at TOGAF®, and Open Group standard, is it really still suitable in fast paced world of the third platform or do we need a third platform version of TOGAF? With Security, for example, there are so many users, so many connections, and the activities of the former Jericho Forum seem like child’s play compared to what you will see around the third platform, so there’s no Forum or Work Group that’s not affected by this Open Platform 3.0 emerging.

With Open Platform 3.0 touching pretty much every aspect of technology and The Open Group, how do you tackle that? Do you have just an umbrella group for everything or look at it through the lens of TOGAF or security or the IT Specialist? How do you attack something so large?

It’s exactly what you just said. It’s fundamentally my belief that we need to do both of these two things. First, we need a catalyst forum, which I would argue is the Open Platform 3.0 Forum, which would be the catalyst platform, the orchestration platform if you like, that would do the overall definitions, the call to action. They’ve already been doing the business scenarios—they set the scene. Then it would be up to this Forum to reach out to all the other Forums and Work Groups to discuss impact and make sure it stays aligned, so here we have an orchestration function of the Open Platform 3.0 Forum. Then, very obviously, all the other Work Groups and Forums need to pick it up and do their own stuff because you cannot aspire to do all of this with one and the same forum because it’s so wide, it’s so diverse. You need to do both.

The Open Platform 3.0 Forum has been working for a year and a half now. What are some of the things the Forum has accomplished thus far?

They’ve been particularly working on some of the key definitions and some of the business scenarios. I would say in order to create an awareness of Open Platform 3.0 in terms of the business value and the definitions, they’ve done a very good job. Next, there needs to be a call to action to get everybody mobilized and setting tangible steps toward the Platform 3.0. I think that’s currently where we are, so that’s good timing, I believe, in terms of what the forum has achieved so far.

Returning to the mission of The Open Group, given all of the awareness we have created, what does it all mean in terms of Boundaryless Information Flow and how does it affect the Forums and Work Groups in The Open Group? That’s what we need to do now.

What are some of the biggest challenges that you see facing adoption of Open Platform 3.0 and standards for that platform?

They are relatively immature technologies. For example, with the Cloud you see a lot of players, a lot of technology providers being quite reluctant to standardize. Some of them are very open about it and are like ‘Right now we are in a niche, and we’re having a lot of fun ourselves, so why open it up right now?’ The movement would be more pressure from the business side saying ‘We want to use your technology but only if you align with some of these emerging standards.’ That would do it or certainly help. This, of course, is what makes The Open Group as powerful as not only technology providers, but also businesses, the enterprises involved and end users of technology. If they work together and created something to mobilize technology providers, that would certainly be a breakthrough, but these are immature technologies and, as I said, with some of these technology providers, it seems more important to them to be a niche player for now and create their own market rather than standardizing on something that their competitors could be on as well.

So this is a sign of a relatively immature industry because every industry that starts to mature around certain topics begins to work around open standards. The more mature we grow in mastering the understanding of the Open Platform 3.0, the more you will see the need for standards arise. It’s all a matter of timing so it’s not so strange that in the past year and a half it’s been very difficult to even discuss standards in this area. But I think we’re entering that era really soon, so it seems to be good timing to discuss it. That’s one important limiting area; I think the providers are not necessarily waiting for it or committed to it.

Secondly, of course, this is a whole next generation of technologies. With all new generations of technologies there are always generation gaps and people in denial or who just don’t feel up to picking it up again or maybe they lack the energy to pick up a new wave of technology and they’re like ‘Why can’t I stay in what I’ve mastered?’ All very understandable. I would call that a very typical IT generation gap that occurs when we see the next generation of IT emerge—sooner or later you get a generation gap, as well. Which has nothing to do with physical age, by the way.

With all these technologies converging so quickly, that gap is going to have to close quickly this time around isn’t it?

Well, there are still mainframes around, so you could argue that there will be two or even three speeds of IT sooner or later. A very stable, robust and predictable legacy environment could even be the first platform that’s more mainframe-oriented, like you see today. A second wave would be that PC workstation, client/server, Internet-based IT landscape, and it has a certain base and certain dynamics. Then you have this third phase, which is the new platform, that is more dynamic and volatile and much more diverse. You could argue that there might be within an organization multiple speeds of IT, multiple speeds of architectures, multi-speed solutioning, and why not choose your own speed?

It probably takes a decade or more to really move forward for many enterprises.

It’s not going as quickly as the Gartners of this world typically thinks it is—in practice we all know it takes longer. So I don’t see any reason why certain people wouldn’t certainly choose deliberately to stay in second gear and don’t go to third gear simply because they think it’s challenging to be there, which is perfectly sound to me and it would bring a lot of work in many years to companies.

That’s an interesting concept because start-ups can easily begin on a new platform but if you’re a company that has been around for a long time and you have existing legacy systems from the mainframe or PC era, those are things that you have to maintain. How do you tackle that as well?

That’s a given in big enterprises. Not everybody can be a disruptive start up. Maybe we all think that we should be like that but it’s not the case in real life. In real life, we have to deal with enterprise systems and enterprise processes and all of them might be very vulnerable to this new wave of challenges. Certainly enterprises can be disruptive themselves if they do it right, but there are always different dynamics, and, as I said, we still have mainframes, as well, even though we declared their ending quite some time ago. The same will happen, of course, to PC-based IT landscapes. It will take a very long time and will take very skilled hands and minds to keep it going and to simplify.

Having said that, you could argue that some new players in the market obviously have the advantage of not having to deal with that and could possibly benefit from a first-mover advantage where existing enterprises have to juggle several balls at the same time. Maybe that’s more difficult, but of course enterprises are enterprises for a good reason—they are big and holistic and mighty, and they might be able to do things that start-ups simply can’t do. But it’s a very unpredictable world, as we all realize, and the third platform brings a lot of disruptiveness.

What’s your perspective on how the Internet of Things will affect all of this?

It’s part of the third platform of course, and it’s something Andras Szakal will be addressing as well. There’s much more coming, both at the input sites, everything is becoming a sensor essentially to where even your wallpaper or paint is a sensor, but on the other hand, in terms of devices that we use to communicate or get information—smart things that whisper in your ears or whatever we’ll have in the coming years—is clearly part of this Platform 3.0 wave that we’ll have as we move away from the PC and the workstation, and there’s a whole bunch of new technologies around to replace it. The Internet of Things is clearly part of it, and we’ll need open standards as well because there are so many different things and devices, and if you don’t create the right standards and platform services to deal with it, it will be a mess. It’s an integral part of the Platform 3.0 wave that we’re seeing.

What is the Open Platform 3.0 Forum going to be working on over the next few months?

Understanding what this Open Platform 3.0 actually means—I think the work we’ve seen so far in the Forum really sets the way in terms of what is it and definitions are growing. Andras will be adding his notion of the Internet of Things and looking at definitions of what is it exactly. Many people already intuitively have an image of it.

The second will be how we deliver value to the business—so the business scenarios are a crucial thing to consider to see how applicable they are, how relevant they are to enterprises. The next thing to do will pertain to work that still needs to be done in The Open Group, as well. What would a new Open Platform 3.0 architecture look like? What are the platform services? What are the ones we can start working on right now? What are the most important business scenarios and what are the platform services that they will require? So architectural impacts, skills impacts, security impacts—as I said, there are very few areas in IT that are not touched by it. Even the new IT4IT Forum that will be launched in October, which is all about methodologies and lifecycle, will need to consider Agile, DevOps-related methodologies because that’s the rhythm and the pace that we’ve got to expect in this third platform. So the rhythm of the working group—definitions, business scenarios and then you start to thinking about what does the platform consist of, what type of services do I need to create to support it and hopefully by then we’ll have some open standards to help accelerate that thinking to help enterprises set a course for themselves. That’s our mission as The Open Group to help facilitate that.

Tolido-RonRon Tolido is Senior Vice President and Chief Technology Officer of Application Services Continental Europe, Capgemini. He is also a Director on the board of The Open Group and blogger for Capgemini’s multiple award-winning CTO blog, as well as the lead author of Capgemini’s TechnoVision and the global Application Landscape Reports. As a noted Digital Transformation ambassador, Tolido speaks and writes about IT strategy, innovation, applications and architecture. Based in the Netherlands, Mr. Tolido currently takes interest in apps rationalization, Cloud, enterprise mobility, the power of open, Slow Tech, process technologies, the Internet of Things, Design Thinking and – above all – radical simplification.

 

 

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The Open Group Panel: Internet of Things – Opportunities and Obstacles

Below is the transcript of The Open Group podcast exploring the challenges and ramifications of the Internet of Things, as machines and sensors collect vast amounts of data.

Listen to the podcast.

Dana Gardner: Hello, and welcome to a special BriefingsDirect thought leadership interview series coming to you in conjunction with recent The Open Group Boston 2014 on July 21 in Boston.

Dana Gardner I’m Dana Gardner, principal analyst at Interarbor Solutions, and I’ll be your host and moderator throughout these discussions on Open Platform 3.0 and Boundaryless Information Flow.

We’re going to now specifically delve into the Internet of Things with a panel of experts. The conference has examined how Open Platform 3.0™ leverages the combined impacts of cloud, big data, mobile, and social. But to each of these now we can add a new cresting wave of complexity and scale as we consider the rapid explosion of new devices, sensors, and myriad endpoints that will be connected using internet protocols, standards and architectural frameworks.

This means more data, more cloud connectivity and management, and an additional tier of “things” that are going to be part of the mobile edge — and extending that mobile edge ever deeper into even our own bodies.

When we think about inputs to these social networks — that’s going to increase as well. Not only will people be tweeting, your device could be very well tweet, too — using social networks to communicate. Perhaps your toaster will soon be sending you a tweet about your English muffins being ready each morning.

The Internet of Things is more than the “things” – it means a higher order of software platforms. For example, if we are going to operate data centers with new dexterity thanks to software-definited networking (SDN) and storage (SDS) — indeed the entire data center being software-defined (SDDC) — then why not a software-defined automobile, or factory floor, or hospital operating room — or even a software-defined city block or neighborhood?

And so how does this all actually work? Does it easily spin out of control? Or does it remain under proper management and governance? Do we have unknown unknowns about what to expect with this new level of complexity, scale, and volume of input devices?

Will architectures arise that support the numbers involved, interoperability, and provide governance for the Internet of Things — rather than just letting each type of device do its own thing?

To help answer some of these questions, The Open Group assembled a distinguished panel to explore the practical implications and limits of the Internet of Things. So please join me in welcoming Said Tabet, Chief Technology Officer for Governance, Risk and Compliance Strategy at EMC, and a primary representative to the Industrial Internet Consortium; Penelope Gordon, Emerging Technology Strategist at 1Plug Corporation; Jean-Francois Barsoum, Senior Managing Consultant for Smarter Cities, Water and Transportation at IBM, and Dave Lounsbury, Chief Technical Officer at The Open Group.

Jean-Francois, we have heard about this notion of “cities as platforms,” and I think the public sector might offer us some opportunity to look at what is going to happen with the Internet of Things, and then extrapolate from that to understand what might happen in the private sector.

Hypothetically, the public sector has a lot to gain. It doesn’t have to go through the same confines of a commercial market development, profit motive, and that sort of thing. Tell us a little bit about what the opportunity is in the public sector for smart cities.

Barsoum_Jean-FrancoisJean-Francois Barsoum: It’s immense. The first thing I want to do is link to something that Marshall Van Alstyne (Professor at Boston University and Researcher at MIT) had talked about, because I was thinking about his way of approaching platforms and thinking about how cities represent an example of that.

You don’t have customers; you have citizens. Cities are starting to see themselves as platforms, as ways to communicate with their customers, their citizens, to get information from them and to communicate back to them. But the complexity with cities is that as a good a platform as they could be, they’re relatively rigid. They’re legislated into existence and what they’re responsible for is written into law. It’s not really a market.

Chris Harding (Forum Director of The Open Group Open Platform 3.0) earlier mentioned, for example, water and traffic management. Cities could benefit greatly by managing traffic a lot better.

Part of the issue is that you might have a state or provincial government that looks after highways. You might have the central part of the city that looks after arterial networks. You might have a borough that would look after residential streets, and these different platforms end up not talking to each other.

They gather their own data. They put in their own widgets to collect information that concerns them, but do not necessarily share with their neighbor. One of the conditions that Marshall said would favor the emergence of a platform had to do with how much overlap there would be in your constituents and your customers. In this case, there’s perfect overlap. It’s the same citizen, but they have to carry an Android and an iPhone, despite the fact it is not the best way of dealing with the situation.

The complexities are proportional to the amount of benefit you could get if you could solve them.

Gardner: So more interoperability issues?

Barsoum: Yes.

More hurdles

Gardner: More hurdles, and when you say commensurate, you’re saying that the opportunity is huge, but the hurdles are huge and we’re not quite sure how this is going to unfold.

Barsoum: That’s right.

Gardner: Let’s go to an area where the opportunity outstrips the challenge, manufacturing. Said, what is the opportunity for the software-defined factory floor for recognizing huge efficiencies and applying algorithmic benefits to how management occurs across domains of supply-chain, distribution, and logistics. It seems to me that this is a no-brainer. It’s such an opportunity that the solution must be found.

Tabet_SaidSaid Tabet: When it comes to manufacturing, the opportunities are probably much bigger. It’s where we can see a lot of progress that has already been done and still work is going on. There are two ways to look at it.

One is the internal side of it, where you have improvements of business processes. For example, similar to what Jean-Francois said, in a lot of the larger companies that have factories all around the world, you’ll see such improvements on a factory base level. You still have those silos at that level.

Now with this new technology, with this connectedness, those improvements are going to be made across factories, and there’s a learning aspect to it in terms of trying to manage that data. In fact, they do a better job. We still have to deal with interoperability, of course, and additional issues that could be jurisdictional, etc.

However, there is that learning that allows them to improve their processes across factories. Maintenance is one of them, as well as creating new products, and connecting better with their customers. We can see a lot of examples in the marketplace. I won’t mention names, but there are lots of them out there with the large manufacturers.

Gardner: We’ve had just-in-time manufacturing and lean processes for quite some time, trying to compress the supply chain and distribution networks, but these haven’t necessarily been done through public networks, the internet, or standardized approaches.

But if we’re to benefit, we’re going to need to be able to be platform companies, not just product companies. How do you go from being a proprietary set of manufacturing protocols and approaches to this wider, standardized interoperability architecture?

Tabet: That’s a very good question, because now we’re talking about that connection to the customer. With the airline and the jet engine manufacturer, for example, when the plane lands and there has been some monitoring of the activity during the whole flight, at that moment, they’ll get that data made available. There could be improvements and maybe solutions available as soon as the plane lands.

Interoperability

That requires interoperability. It requires Platform 3.0 for example. If you don’t have open platforms, then you’ll deal with the same hurdles in terms of proprietary technologies and integration in a silo-based manner.

Gardner: Penelope, you’ve been writing about the obstacles to decision-making that might become apparent as big data becomes more prolific and people try to capture all the data about all the processes and analyze it. That’s a little bit of a departure from the way we’ve made decisions in organizations, public and private, in the past.

Of course, one of the bigger tenets of Internet of Things is all this great data that will be available to us from so many different points. Is there a conundrum of some sort? Is there an unknown obstacle for how we, as organizations and individuals, can deal with that data? Is this going to be chaos, or is this going to be all the promises many organizations have led us to believe around big data in the Internet of Things?

Gordon_PenelopePenelope Gordon: It’s something that has just been accelerated. This is not a new problem in terms of the decision-making styles not matching the inputs that are being provided into the decision-making process.

Former US President Bill Clinton was known for delaying making decisions. He’s a head-type decision-maker and so he would always want more data and more data. That just gets into a never-ending loop, because as people collect data for him, there is always more data that you can collect, particularly on the quantitative side. Whereas, if it is distilled down and presented very succinctly and then balanced with the qualitative, that allows intuition to come to fore, and you can make optimal decisions in that fashion.

Conversely, if you have someone who is a heart-type or gut-type decision-maker and you present them with a lot of data, their first response is to ignore the data. It’s just too much for them to take in. Then you end up completely going with whatever you feel is correct or whatever you have that instinct that it’s the correct decision. If you’re talking about strategic decisions, where you’re making a decision that’s going to influence your direction five years down the road, that could be a very wrong decision to make, a very expensive decision, and as you said, it could be chaos.

It just brings to mind to me Dr. Suess’s The Cat in the Hat with Thing One and Thing Two. So, as we talk about the Internet of Things, we need to keep in mind that we need to have some sort of structure that we are tying this back to and understanding what are we trying to do with these things.

Gardner: Openness is important, and governance is essential. Then, we can start moving toward higher-order business platform benefits. But, so far, our panel has been a little bit cynical. We’ve heard that the opportunity and the challenges are commensurate in the public sector and that in manufacturing we’re moving into a whole new area of interoperability, when we think about reaching out to customers and having a boundary that is managed between internal processes and external communications.

And we’ve heard that an overload of data could become a very serious problem and that we might not get benefits from big data through the Internet of Things, but perhaps even stumble and have less quality of decisions.

So Dave Lounsbury of The Open Group, will the same level of standardization work? Do we need a new type of standards approach, a different type of framework, or is this a natural path and course what we have done in the past?

Different level

Lounsbury_DaveDave Lounsbury: We need to look at the problem at a different level than we institutionally think about an interoperability problem. Internet of Things is riding two very powerful waves, one of which is Moore’s Law, that these sensors, actuators, and network get smaller and smaller. Now we can put Ethernet in a light switch right, a tag, or something like that.

Also, Metcalfe’s Law that says that the value of all this connectivity goes up with the square of the number of connected points, and that applies to both the connection of the things but more importantly the connection of the data.

The trouble is, as we have said, that there’s so much data here. The question is how do you manage it and how do you keep control over it so that you actually get business value from it. That’s going to require us to have this new concept of a platform to not only to aggregate, but to just connect the data, aggregate it, correlate it as you said, and present it in ways that people can make decisions however they want.

Also, because of the raw volume, we have to start thinking about machine agency. We have to think about the system actually making the routine decisions or giving advice to the humans who are actually doing it. Those are important parts of the solution beyond just a simple “How do we connect all the stuff together?”

Gardner: We might need a higher order of intelligence, now that we have reached this border of what we can do with our conventional approaches to data, information, and process.

Thinking about where this works best first in order to then understand where it might end up later, I was intrigued again this morning by Professor Van Alstyne. He mentioned that in healthcare, we should expect major battles, that there is a turf element to this, that the organization, entity or even commercial corporation that controls and manages certain types of information and access to that information might have some very serious platform benefits.

The openness element now is something to look at, and I’ll come back to the public sector. Is there a degree of openness that we could legislate or regulate to require enough control to prevent the next generation of lock-in, which might not be to a platform to access to data information and endpoints? Where is it in the public sector that we might look to a leadership position to establish needed openness and not just interoperability.

Barsoum: I’m not even sure where to start answering that question. To take healthcare as an example, I certainly didn’t write the bible on healthcare IT systems and if someone did write that, I think they really need to publish it quickly.

We have a single-payer system in Canada, and you would think that would be relatively easy to manage. There is one entity that manages paying the doctors, and everybody gets covered the same way. Therefore, the data should be easily shared among all the players and it should be easy for you to go from your doctor, to your oncologist, to whomever, and maybe to your pharmacy, so that everybody has access to this same information.

We don’t have that and we’re nowhere near having that. If I look to other areas in the public sector, areas where we’re beginning to solve the problem are ones where we face a crisis, and so we need to address that crisis rapidly.

Possibility of improvement

In the transportation infrastructure, we’re getting to that point where the infrastructure we have just doesn’t meet the needs. There’s a constraint in terms of money, and we can’t put much more money into the structure. Then, there are new technologies that are coming in. Chris had talked about driverless cars earlier. They’re essentially throwing a wrench into the works or may be offering the possibility of improvement.

On any given piece of infrastructure, you could fit twice as many driverless cars as cars with human drivers in them. Given that set of circumstances, the governments are going to find they have no choice but to share data in order to be able to manage those. Are there cases where we could go ahead of a crisis in order to manage it? I certainly hope so.

Gardner: How about allowing some of the natural forces of marketplaces, behavior, groups, maybe even chaos theory, where if sufficient openness is maintained there will be some kind of a pattern that will emerge? We need to let this go through its paces, but if we have artificial barriers, that might be thwarted or power could go to places that we would regret later.

Barsoum: I agree. People often focus on structure. So the governance doesn’t work. We should find some way to change the governance of transportation. London has done a very good job of that. They’ve created something called Transport for London that manages everything related to transportation. It doesn’t matter if it’s taxis, bicycles, pedestrians, boats, cargo trains, or whatever, they manage it.

You could do that, but it requires a lot of political effort. The other way to go about doing it is saying, “I’m not going to mess with the structures. I’m just going to require you to open and share all your data.” So, you’re creating a new environment where the governance, the structures, don’t really matter so much anymore. Everybody shares the same data.

Gardner: Said, to the private sector example of manufacturing, you still want to have a global fabric of manufacturing capabilities. This is requiring many partners to work in concert, but with a vast new amount of data and new potential for efficiency.

How do you expect that openness will emerge in the manufacturing sector? How will interoperability play when you don’t have to wait for legislation, but you do need to have cooperation and openness nonetheless?

Tabet: It comes back to the question you asked Dave about standards. I’ll just give you some examples. For example, in the automotive industry, there have been some activities in Europe around specific standards for communication.

The Europeans came to the US and started to have discussions, and the Japanese have interest, as well as the Chinese. That shows, because there is a common interest in creating these new models from a business standpoint, that these challenges they have to be dealt with together.

Managing complexity

When we talk about the amounts of data, what we call now big data, and what we are going to see in about five years or so, you can’t even imagine. How do we manage that complexity, which is multidimensional? We talked about this sort of platform and then further, that capability and the data that will be there. From that point of view, openness is the only way to go.

There’s no way that we can stay away from it and still be able to work in silos in that new environment. There are lots of things that we take for granted today. I invite some of you to go back and read articles from 10 years ago that try to predict the future in technology in the 21st century. Look at your smart phones. Adoption is there, because the business models are there, and we can see that progress moving forward.

Collaboration is a must, because it is a multidimensional level. It’s not just manufacturing like jet engines, car manufacturers, or agriculture, where you have very specific areas. They really they have to work with their customers and the customers of their customers.

Adoption is there, because the business models are there, and we can see that progress moving forward.

Gardner: Dave, I have a question for both you and Penelope. I’ve seen some instances where there has been a cooperative endeavor for accessing data, but then making it available as a service, whether it’s an API, a data set, access to a data library, or even analytics applications set. The Ocean Observatories Initiative is one example, where it has created a sensor network across the oceans and have created data that then they make available.

Do you think we expect to see an intermediary organization level that gets between the sensors and the consumers or even controllers of the processes? Is there’s a model inherent in that that we might look to — something like that cooperative data structure that in some ways creates structure and governance, but also allows for freedom? It’s sort of an entity that we don’t have yet in many organizations or many ecosystems and that needs to evolve.

Lounsbury: We’re already seeing that in the marketplace. If you look at the commercial and social Internet of Things area, we’re starting to see intermediaries or brokers cropping up that will connect the silo of my android ecosystem to the ecosystem of package tracking or something like that. There are dozens and dozens of these cropping up.

In fact, you now see APIs even into a silo of what you might consider a proprietary system and what people are doing is to to build a layer on top of those APIs that intermediate the data.

This is happening on a point-to-point basis now, but you can easily see the path forward. That’s going to expand to large amounts of data that people will share through a third party. I can see this being a whole new emerging market much as what Google did for search. You could see that happening for the Internet of Things.

Gardner: Penelope, do you have any thoughts about how that would work? Is there a mutually assured benefit that would allow people to want to participate and cooperate with that third entity? Should they have governance and rules about good practices, best practices for that intermediary organization? Any thoughts about how data can be managed in this sort of hierarchical model?

Nothing new

Gordon: First, I’ll contradict it a little bit. To me, a lot of this is nothing new, particularly coming from a marketing strategy perspective, with business intelligence (BI). Having various types of intermediaries, who are not only collecting the data, but then doing what we call data hygiene, synthesis, and even correlation of the data has been around for a long time.

It was an interesting, when I looked at recent listing of the big-data companies, that some notable companies were excluded from that list — companies like Nielsen. Nielsen’s been collecting data for a long time. Harte-Hanks is another one that collects a tremendous amount of information and sells that to companies.

That leads into the another part of it that I think there’s going to be. We’re seeing an increasing amount of opportunity that involves taking public sources of data and then providing synthesis on it. What remains to be seen is how much of the output of that is going to be provided for “free”, as opposed to “fee”. We’re going to see a lot more companies figuring out creative ways of extracting more value out of data and then charging directly for that, rather than using that as an indirect way of generating traffic.

Gardner: We’ve seen examples of how this has been in place. Does it scale and does the governance or lack of governance that might be in the market now sustain us through the transition into Platform 3.0 and the Internet of Things.

Gordon: That aspect is the lead-on part of “you get what you pay for”. If you’re using a free source of data, you don’t have any guarantee that it is from authoritative sources of data. Often, what we’re getting now is something somebody put it in a blog post, and then that will get referenced elsewhere, but there was nothing to go back to. It’s the shaky supply chain for data.

You need to think about the data supply and that is where the governance comes in. Having standards is going to increasingly become important, unless we really address a lot of the data illiteracy that we have. A lot of people do not understand how to analyze data.

One aspect of that is a lot of people expect that we have to do full population surveys, as opposed representative sampling to get much more accurate and much more cost-effective collection of data. That’s just one example, and we do need a lot more in governance and standards.

Gardner: What would you like to see changed most in order for the benefits and rewards of the Internet of Things to develop and overcome the drawbacks, the risks, the downside? What, in your opinion, would you like to see happen to make this a positive, rapid outcome? Let’s start with you Jean-Francois.

Barsoum: There are things that I have seen cities start to do now. There are couple of examples: Philadelphia is one and Barcelona does this too. Rather than do the typical request for proposal (RFP), where they say, “This is the kind of solution we’re looking for, and here are our parameters. Can l you tell us how much it is going to cost to build,” they come to you with the problem and they say, “Here is the problem I want to fix. Here are my priorities, and you’re at liberty to decide how best to fix the problem, but tell us how much that would cost.”

If you do that and you combine it with access to the public data that is available — if public sector opens up its data — you end up with a very powerful combination that liberates a lot of creativity. You can create a lot of new business models. We need to see much more of that. That’s where I would start.

More education

Tabet: I agree with Jean-Francois on that. What I’d like to add is that I think we need to push the relation a little further. We need more education, to your point earlier, around the data and the capabilities.

We need these platforms that we can leverage a little bit further with the analytics, with machine learning, and with all of these capabilities that are out there. We have to also remember, when we talk about the Internet of Things, it is things talking to each other.

So it is not human-machine communication. Machine-to-machine automation will be further than that, and we need more innovation and more work in this area, particularly more activity from the governments. We’ve seen that, but it is a little bit frail from that point of view right now.

Gardner: Dave Lounsbury, thoughts about what need to happen in order to keep this on the tracks?

Lounsbury: We’ve touched on lot of them already. Thank you for mentioning the machine-to-machine part, because there are plenty of projections that show that it’s going to be the dominant form of Internet communication, probably within the next four years.

So we need to start thinking of that and moving beyond our traditional models of humans talking through interfaces to set of services. We need to identify the building blocks of capability that you need to manage, not only the information flow and the skilled person that is going to produce it, but also how you manage the machine-to-machine interactions.

Gordon: I’d like to see not so much focus on data management, but focus on what is the data managing and helping us to do. Focusing on the machine-to-machine and the devices is great, but it should be not on the devices or on the machines… it should be on what can they accomplish by communicating; what can you accomplish with the devices and then have a reverse engineer from that.

Gardner: Let’s go to some questions from the audience. The first one asks about a high order of intelligence which we mentioned earlier. It could be artificial intelligence, perhaps, but they ask whether that’s really the issue. Is the nature of the data substantially different, or we are just creating more of the same, so that it is a storage, plumbing, and processing problem? What, if anything, are we lacking in our current analytics capabilities that are holding us back from exploiting the Internet of Things?

Gordon: I’ve definitely seen that. That has a lot to do with not setting your decision objectives and your decision criteria ahead of time so that you end up collecting a whole bunch of data, and the important data gets lost in the mix. There is a term “data smog.”

Most important

The solution is to figure out, before you go collecting data, what data is most important to you. If you can’t collect certain kinds of data that are important to you directly, then think about how to indirectly collect that data and how to get proxies. But don’t try to go and collect all the data for that. Narrow in on what is going to be most important and most representative of what you’re trying to accomplish.

Gardner: Does anyone want to add to this idea of understanding what current analytics capabilities are lacking, if we have to adopt and absorb the Internet of Things?

Barsoum: There is one element around projection into the future. We’ve been very good at analyzing historical information to understand what’s been happening in the past. We need to become better at projecting into the future, and obviously we’ve been doing that for some time already.

But so many variables are changing. Just to take the driverless car as an example. We’ve been collecting data from loop detectors, radar detectors, and even Bluetooth antennas to understand how traffic moves in the city. But we need to think harder about what that means and how we understand the city of tomorrow is going to work. That requires more thinking about the data, a little bit like what Penelope mentioned, how we interpret that, and how we push that out into the future.

Lounsbury: I have to agree with both. It’s not about statistics. We can use historical data. It helps with lot of things, but one of the major issues we still deal with today is the question of semantics, the meaning of the data. This goes back to your point, Penelope, around the relevance and the context of that information – how you get what you need when you need it, so you can make the right decisions.

Gardner: Our last question from the audience goes back to Jean-Francois’s comments about the Canadian healthcare system. I imagine it applies to almost any healthcare system around the world. But it asks why interoperability is so difficult to achieve, when we have the power of the purse, that is the market. We also supposedly have the power of the legislation and regulation. You would think between one or the other or both that interoperability, because the stakes are so high, would happen. What’s holding it up?

Barsoum: There are a couple of reasons. One, in the particular case of healthcare, is privacy, but that is one that you could see going elsewhere. As soon as you talk about interoperability in the health sector, people start wondering where is their data going to go and how accessible is it going to be and to whom.

You need to put a certain number of controls over top of that. What is happening in parallel is that you have people who own some data, who believe they have some power from owning that data, and that they will lose that power if they share it. That can come from doctors, hospitals, anywhere.

So there’s a certain amount of change management you have to get beyond. Everybody has to focus on the welfare of the patient. They have to understand that there has to be a priority, but you also have to understand the welfare of the different stakeholders in the system and make sure that you do not forget about them, because if you forget about them they will find some way to slow you down.

Use of an ecosystem

Lounsbury: To me, that’s a perfect example of what Marshall Van Alstyne talked about this morning. It’s the change from focus on product to a focus on an ecosystem. Healthcare traditionally has been very focused on a doctor providing product to patient, or a caregiver providing a product to a patient. Now, we’re actually starting to see that the only way we’re able to do this is through use of an ecosystem.

That’s a hard transition. It’s a business-model transition. I will put in a plug here for The Open Group Healthcare vertical, which is looking at that from architecture perspective. I see that our Forum Director Jason Lee is over here. So if you want to explore that more, please see him.

Gardner: I’m afraid we will have to leave it there. We’ve been discussing the practical implications of the Internet of Things and how it is now set to add a new dimension to Open Platform 3.0 and Boundaryless Information Flow.

We’ve heard how new thinking about interoperability will be needed to extract the value and orchestrate out the chaos with such vast new scales of inputs and a whole new categories of information.

So with that, a big thank you to our guests: Said Tabet, Chief Technology Officer for Governance, Risk and Compliance Strategy at EMC; Penelope Gordon, Emerging Technology Strategist at 1Plug Corp.; Jean-Francois Barsoum, Senior Managing Consultant for Smarter Cities, Water and Transportation at IBM, and Dave Lounsbury, Chief Technology Officer at The Open Group.

This is Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator throughout these discussions on Open Platform 3.0 and Boundaryless Information Flow at The Open Group Conference, recently held in Boston. Thanks again for listening, and come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript.

Transcript of The Open Group podcast exploring the challenges and ramifications of the Internet of Things, as machines and sensors collect vast amounts of data. Copyright The Open Group and Interarbor Solutions, LLC, 2005-2014. All rights reserved.

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Filed under Boundaryless Information Flow™, Business Architecture, Cloud, Cloud/SOA, Data management, digital technologies, Enterprise Architecture, Future Technologies, Information security, Internet of Things, Interoperability, Open Platform 3.0, Service Oriented Architecture, Standards, Strategy, Supply chain risk, Uncategorized

Q&A with Marshall Van Alstyne, Professor, Boston University School of Management and Research Scientist MIT Center for Digital Business

By The Open Group

The word “platform” has become a nearly ubiquitous term in the tech and business worlds these days. From “Platform as a Service” (PaaS) to IDC’s Third Platform to The Open Group Open Platform 3.0™ Forum, the concept of platforms and building technology frames and applications on top of them has become the next “big thing.”

Although the technology industry tends to conceive of “platforms” as the vehicle that is driving trends such as mobile, social networking, the Cloud and Big Data, Marshall Van Alstyne, Professor at Boston University’s School of Management and a Research Scientist at the MIT Center for Digital Business, believes that the radical shifts that platforms bring are not just technological.

We spoke with Van Alstyne prior to The Open Group Boston 2014, where he presented a keynote, about platforms, how they have shifted traditional business models and how they are impacting industries everywhere.

The title of your session at the Boston conference was “Platform Shift – How New Open Business Models are Changing the Shape of Industry.” How would you define both “platform” and “open business model”?

I think of “platform” as a combination of two things. One, a set of standards or components that folks can take up and use for production of goods and services. The second thing is the rules of play, or the governance model – who has the ability to participate, how do you resolve conflict, and how do you divide up the royalty streams, or who gets what? You can think of it as the two components of the platform—the open standard together with the governance model. The technologists usually get the technology portion of it, and the economists usually get the governance and legal portions of it, but you really need both of them to understand what a ‘platform’ is.

What is the platform allowing then and how is that different from a regular business model?

The platform allows third parties to conduct business using system resources so they can actually meet and exchange goods across the platform. Wonderful examples of that include AirBnB where you can rent rooms or you can post rooms, or eBay, where you can sell goods or exchange goods, or iTunes where you can go find music, videos, apps and games provided by others, or Amazon where third parties are even allowed to set up shop on top of Amazon. They have moved to a business model where they can take control of the books in addition to allowing third parties to sell their own books and music and products and services through the Amazon platform. So by opening it up to allow third parties to participate, you facilitate exchange and grow a market by helping that exchange.

How does this relate to the concept of the technology industry is defining the “third platform”?

I think of it slightly differently. The tech industry uses mobile and social and cloud and data to characterize it. In some sense this view offers those as the attributes that characterize platforms or the knowledge base that enable platforms. But we would add to that the economic forces that actually shape platforms. What we want to do is give you some of the strategic tools, the incentives, the rules that will actually help you control their trajectory by helping you improve who participates and then measure and improve the value they contribute to the platform. So a full ecosystem view is not just the technology and the data, it also measures the value and how you divide that value. The rules of play really become important.

I think the “third platform” offers marvelous concepts and attributes but you also need to add the economics to it: Why do you participate, who gets what portions of the value, and who ultimately owns control.

Who does control the platform then?

A platform has multiple parts. Determining who controls what part is the art and design of the governance model. You have to set up control in the right way to motivate people to participate. But before we get to that, let’s go back and complete the idea of what’s an ‘open platform.’

To define an open platform, consider both the right of access and the right to manipulate platform resources, then consider granting those rights to four different parties. One is the user—can they access one another, can they access data, can they access system resources? Another group is developers—can they manipulate system resources, can they add new features to it, can they sell through the platform? The third group is the platform providers. You often think of them as those folks that facilitate access across the platform. To give you an example, iTunes is a single monolithic store, so the provider is simply Apple, but Android, in contrast, allows multiple providers, so there’s a Samsung Android store, an LTC Android store, a Google Android store, there’s even an Amazon version that uses a different version of Android. So that platform has multiple providers each with rights to access users. The fourth group is the party that controls the underlying property rights, who owns the IP. The ability modify the underlying standard and also the rights of access for other parties is the bottom-most layer.

So to answer the question of what is ‘open,’ you have to consider the rights of access of all four groups—the users, developers, the providers and IP rights holders, or sponsors, underneath.

Popping back up a level, we’re trying to motivate different parties to participate in the ecosystem. So what do you give the users? Usually it’s some kind of value. What do you give developers? Usually it’s some set of SDKs and APIs, but also some level of royalties. It’s fascinating. If you look back historically, Amazon initially tried a publishing royalty where they took 70% and gave a minority 30% back to developers. They found that didn’t fly very well and they had to fall back to the app store or software-style royalty, where they’re taking a lower percentage. I think Apple, for example, takes 30 percent, and Amazon is now close to that. You see ranges of royalties going anywhere from a few percent—an example is credit cards—all the way up to iStock photo where they take roughly 70 percent. That’s an extremely high rate, and one that I don’t recommend. We were just contracting for designs at 99Designs and they take a 20 percent cut. That’s probably more realistic, but lower might perhaps even be better—you can create stronger network effect if that’s the case.

Again, the real question of control is how you motivate third parties to participate and add value? If you are allowing them to use resources to create value and keep a lot of that value, then they’re more motivated to participate, to invest, to bring their resources to your platform. If you take most of the value they create, they won’t participate. They won’t add value. One of the biggest challenges for open platforms—what you might call the ‘Field of Dreams’ approach—is that most folks open their platform and assume ‘if you build it, they will come,’ but you really need to reward them to do so. Why would they want to come build with you? There are numerous instances of platforms that opened but no developer chooses to add value—the ecosystem is too small. You have to solve the chicken and egg problem where if you don’t have users, developers don’t want to build for you, but if you don’t have developer apps, then why do users participate? So you’ve got a huge feedback problem. And those are where the economics become critical, you must solve the chicken and egg problem to build and roll out platforms.

It’s not just a technology question; it’s also an economics and rewards question.

Then who is controlling the platform?

The answer depends on the type of platform. Giving different groups a different set of rights creates different types of platform. Consider the four different parties: users, developers, providers, and sponsors. At one extreme, the Apple Mac platform of the 1980s reserved most rights for development, for producing hardware (the provider layer), and for modifying the IP (the sponsor layer) all to Apple. Apple controlled the platform and it remained closed. In contrast, Microsoft relaxed platform control in specific ways. It licensed to multiple providers, enabling Dell, HP, Compaq and others to sell the platform. It gave developers rights of access to SDKs and APIs, enabling them to extend the platform. These control choices gave Microsoft more than six times the number of developers and more than twenty times the market share of Apple at the high point of Microsoft’s dominance of desktop operating systems. Microsoft gave up some control in order to create a more inclusive platform and a much bigger market.

Control is not a single concept. There are many different control rights you can grant to different parties. For example, you often want to give users an ability to control their own data. You often want to give developers intellectual property rights for the apps that they create and often over the data that their users create. You may want to give them some protections against platform misappropriation. Developers resent it if you take their ideas. So if the platform sees a really clever app that’s been built on top of its platform, what’s the guarantee that the platform simply doesn’t take it or build a competing app? You need to protect your developers in that case. Same thing’s true of the platform provider—what guarantees do they provide users for the quality of content provided on their ecosystem? For example, the Android ecosystem is much more open than the iPhone ecosystem, which means you have more folks offering stores. Simultaneously, that means that there are more viruses and more malware in Android, so what rights and guarantees do you require of the platform providers to protect the users in order that they want to participate? And then at the bottom, what rights do other participants have to control the direction of the platform growth? In the Visa model, for example, there are multiple member banks that help to influence the general direction of that credit card standard. Usually the most successful platforms have a single IP rights holder, but there are several examples of that have multiple IP rights holders.

So, in the end control defines the platform as much as the platform defines control.

What is the “secret” of the Internet-driven marketplace? Is that indeed the platform?

The secret is that, in effect, the goal of the platform is to increase transaction volume and value. If you can do that—and we can give you techniques for doing it—then you can create massive scale. Increasing the transaction value and transactions volume across your platform means that the owner of the platform doesn’t have to be the sole source of content and new ideas provided on the platform. If the platform owner is the only source of value then the owner is also the bottleneck. The goal is to consummate matches between producers and consumers of value. You want to help users find the content, find the resources, find the other people that they want to meet across your platform. In Apple’s case, you’re helping them find the music, the video, the games, and the apps that they want. In AirBnB’s case, you’re helping them find the rooms that they want, or Uber, you’re helping them find a driver. On Amazon, the book recommendations help you find the content that you want. In all the truly successful platforms, the owner of the platform is not providing all of that value. They’re enabling third parties to add that value, and that’s one reasy why The Open Group’s ideas are so important—you need open systems for this to happen.

What’s wrong with current linear business models? Why is a network-driven approach superior?

The fundamental reason why the linear business model no longer works is that it does not manage network effects. Network effects allow you to build platforms where users attract other users and you get feedback that grows your system. As more users join your platform, more developers join your platform, which attracts more users, which attracts more developers. You can see it on any of the major platforms. This is also true of Google. As advertisers use Google Search, the algorithms get better, people find the content that they want, so more advertisers use it. As more drivers join Uber, more people are happier passengers, which attracts more drivers. The more merchants accept Visa, the more consumers are willing to carry it, which attracts more merchants, which attracts more consumers. You get positive feedback.

The consequence of that is that you tend to get market concentration—you get winner take all markets. That’s where platforms dominate. So you have a few large firms within a given category, whether this is rides or books or hotels or auctions. Further, once you get network effects changing your business model, the linear insights into pricing, into inventory management, into innovation, into strategy breakdown.

When you have these multi-sided markets, pricing breaks down because you often price differently to one side than another because one side attracts the other. Inventory management practices breakdown because you’re selling inventory that you don’t even own. Your R&D strategies breakdown because now you’re motivating innovation and research outside the boundaries of the firm, as opposed to inside the internal R&D group. And your strategies breakdown because you’re not just looking for cost leadership or product differentiation, now you’re looking to shape the network effects as you create barriers to entry.

One of the things that I really want to argue strenuously is that in markets where platforms will emerge, platforms beat product every time. So the platform business model will inevitably beat the linear, product-based business model. Because you’re harnessing new forces in order to develop a different kind of business model.

Think of it the following way–imagine that value is growing as users consume your product. Think of any of the major platforms, as more folks use Google, search gets better, the more recommendations improve on Amazon, and the easier it is to find a ride on Uber, so more folks want to be on there. It is easier to scale network effects outside your business than inside your business. There’s simply more people outside than inside. The moment that happens, the locus control, the locus of innovation, moves from inside the firm to outside the firm. So the rules change. Pricing changes, your innovation strategies change, your inventory policies change, your R&D changes. You’re now managing resources outside the firm, rather than inside, in order to capture scale. This is different than the traditional industrial supply economies of scale.

Old systems are giving away to new systems. It’s not that the whole system breaks down, it’s simply that you’re looking to manage network effects and manage new business models. Another way to see this is that previously you were managing capital. In the industrial era, you were managing steel, you were managing large amounts of finance in banking, you were managing auto parts—huge supply economies of scale. In telecommunications, you were managing infrastructure. Now, you’re managing communities and these are managed outside the firm. The value that’s been created at Facebook or WhatsApp or Instagram or any of the new acquisitions, it’s not the capital that’s critical, it’s the communities that are critical, and these are built outside the firm.

There is a lot of talk in the industry about the Nexus of Forces as Gartner calls it, or Third Platform (IDC). The Open Group calls it Open Platform 3.0. Your concept goes well beyond technology—how does Open Platform 3.0 enable new business models?

Those are the enablers—they’re shall we say necessary, but they’re not sufficient. You really must harness the economic forces in addition to those enablers—mobile, social, Cloud, data. You must manage communities outside the firm, that’s the mobile and the social element of it. But this also involves designing governance and setting incentives. How are you capturing users outside the organization, how are they contributing, how are they being motivated to participate, why are they spreading your products to their peers? The Cloud allows it to scale—so Instagram and What’s App and others scale. Data allows you to “consummate the match.” You use that data to help people find what they need, to add value, so all of those things are the enablers. Then you have to harness the economics of the enablers to encourage people to do the right thing. You can see the correct intuition if you simply ask what happens if all you offer is a Cloud service and nothing more. Why will anyone use it? What’s the value to that system? If you open APIs to it, again, if you don’t have a user base, why are developers going to contribute? Developers want to reach users. Users want valuable functionality.

You must manage the motives and the value-add on the platform. New business models come from orchestrating not just the technology but also the third party sources of value. One of the biggest challenges is to grow these businesses from scratch—you’ve got the cold start chicken and egg problem. You don’t have network effects if you don’t have a user base, if you don’t have users, you don’t have network effects.

Do companies need to transform themselves into a “business platform” to succeed in this new marketplace? Are there industries immune to this shift?

There is a continuum of companies that are going to be affected. It starts at one end with companies that are highly information intense—anything that’s an information intensive business will be dramatically affected, anything that’s community or fashion-based business will be dramatically affected. Those include companies involved in media and news, songs, music, video; all of those are going to be the canaries in the coalmine that see this first. Moving farther along will be those industries that require some sort of certification—those include law and medicine and education—those, too, will also be platformized, so the services industries will become platforms. Farther down that are the ones that are heavily, heavily capital intensive where control of physical capital is paramount, those include trains and oil rigs and telecommunications infrastructure—eventually those will be affected by platform business models to the extent that data helps them gain efficiencies or add value, but they will in some sense be the last to be affected by platform business models. Look for the businesses where the cost side is shrinking in proportion to the delivery of value and where the network effects are rising as a proportional increase in value. Those forces will help you predict which industries will be transformed.

How can Enterprise Architecture be a part of this and how do open standards play a role?

The second part of that question is actually much easier. How do open standards play a role? The open standards make it much easier for third parties to attach and incorporate technology and features such that they can in turn add value. Open standards are essential to that happening. You do need to ask the question as to who controls those standards—is it completely open or is it a proprietary standard, a published standard but it’s not manipulable by a third party.

There will be at least two or three different things that Enterprise Architects need to do. One of these is to design modular components that are swappable, so as better systems become available, the better systems can be swapped in. The second element will be to watch for components of value that should be absorbed into the platform itself. As an example, in operating systems, web browsing has effectively been absorbed into the platform, streaming has been absorbed into the platform so that they become aware of how that actually works. A third thing they need to do is talk to the legal team to see where it is that the third parties property rights can be protected so that they invest. One of the biggest mistakes that firms make is to simply assume that because they own the platform, because they have the rights of control, that they can do what they please. If they do that, they risk alienating their ecosystems. So they should talk to their potential developers to incorporate developer concerns. One of my favorite examples is the Intel Architecture Lab which has done a beautiful job of articulating the voices of developers in their own architectural plans. A fourth thing that can be done is an idea borrowed from SAP, that builds Enterprise Architecture—they articulate an 18-24 month roadmap where they say these are the features that are coming, so you can anticipate and build on those. Also it gives you an idea of what features will be safe to build on so you won’t lose the value you’ve created.

What can companies do to begin opening their business models and more easily architect that?

What they should do is to consider four groups articulated earlier— those are the users, the providers, the developers and the sponsors—each serve a different role. Firms need to understand what their own role will be in order that they can open and architect the other roles within their ecosystem. They’ll also need to choose what levels of exclusivity they need to give their ecosystem partners in a different slice of the business. They should also figure out which of those components they prefer to offer themselves as unique competencies and where they need to seek third party assistance, either in new ideas or new resources or even new marketplaces. Those factors will help guide businesses toward different kinds of partnerships, and they’ll have to be open to those kinds of partners. In particular, they should think about where are they most likely to be missing ideas or missing opportunities. Those technical and business areas should open in order that third parties can take advantage of those opportunities and add value.

 

vanalstynemarshallProfessor Van Alstyne is one of the leading experts in network business models. He conducts research on information economics, covering such topics as communications markets, the economics of networks, intellectual property, social effects of technology, and productivity effects of information. As co-developer of the concept of “two sided networks” he has been a major contributor to the theory of network effects, a set of ideas now taught in more than 50 business schools worldwide.

Awards include two patents, National Science Foundation IOC, SGER, SBIR, iCorp and Career Awards, and six best paper awards. Articles or commentary have appeared in Science, Nature, Management Science, Harvard Business Review, Strategic Management Journal, The New York Times, and The Wall Street Journal.

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Discussing Enterprise Decision-Making with Penelope Everall Gordon

By The Open Group

Most enterprises today are in the process of jumping onto the Big Data bandwagon. The promise of Big Data, as we’re told, is that if every company collects as much data as they can—about everything from their customers to sales transactions to their social media feeds—executives will have “the data they need” to make important decisions that can make or break the company. Not collecting and using your data, as the conventional wisdom has it, can have deadly consequences for any business.

As is often the case with industry trends, the hype around Big Data contains both a fair amount of truth and a fair amount of fuzz. The problem is that within most organizations, that conventional wisdom about the power of data for decision-making is usually just the tip of the iceberg when it comes to how and why organizational decisions are made.

According to Penelope Gordon, a consultant for 1Plug Corporation who was recently a Cloud Strategist at Verizon and was formerly a Service Product Strategist at IBM, that’s why big “D” (Data) needs to be put back into the context of enterprise decision-making. Gordon, who spoke at The Open Group Boston 2014, in the session titled “Putting the D Back in Decision” with Jean-Francois Barsoum of IBM, argues that a focus on collecting a lot of data has the potential to get in the way of making quality decisions. This is, in part, due to the overabundance of data that’s being collected under the assumption that you never know where there’s gold to be mined in your data, so if you don’t have all of it at hand, you may just miss something.

Gordon says that assuming the data will make decisions obvious also ignores the fact that ultimately decisions are made by people—and people usually make decisions based on their own biases. According to Gordon, there are three types of natural decision making styles—heart, head and gut styles—corresponding to different personality types, she said; the greater the amount of data the more likely that it will not balance the natural decision-making style.

Head types, Gordon says, naturally make decisions based on quantitative evidence. But what often happens is that head types often put off making a decision until more data can be collected, wanting more and more data so that they can make the best decision based on the facts. She cites former President Bill Clinton as a classic example of this type. During his presidency, he was famous for putting off decision-making in favor of gathering more and more data before making the decision, she says. Relying solely on quantitative data also can mean you may miss out on other important factors in making optimal decisions based on either heart (qualitative) or instinct. Conversely, a gut-type presented with too much data will likely just end up ignoring data and acting on instinct, much like former President George W. Bush, Gordon says.

Gordon believes part of the reason that data and decisions are more disconnected than one might think is because IT and Marketing departments have become overly enamored with what technology can offer. These data providers need to step back and first examine the decision objectives as well as the governance behind those decisions. Without understanding the organization’s decision-making processes and the dynamics of the decision-makers, it can be difficult to make optimal and effective strategic recommendations, she says, because you don’t have the full picture of what the stakeholders will or will not accept in terms of a recommendation, data or no data.

Ideally, Gordon says, you want to get to a point where you can get to the best decision outcome possible by first figuring out the personal and organizational dynamics driving decisions within the organization, shifting the focus from the data to the decision for which the data is an input.

“…what you’re trying to do is get the optimal outcome, so your focus needs to be on the outcome, so when you’re collecting the data and assessing the data, you also need to be thinking about ‘how am I going to present this data in a way that it is going to be impactful in improving the decision outcomes?’ And that’s where the governance comes into play,” she said.

Governance is of particular importance now, Gordon says, because decisions are increasingly being made by individual departments, such as when departments buy their own cloud-enabled services, such as sales force automation. In that case, an organization needs to have a roadmap in place with compensation to incent decision-makers to adhere to that roadmap and decision criteria for buying decisions, she said.

Gordon recommends that companies put in place 3-5 top criteria for each decision that needs to be made so that you can ensure that the decision objectives are met. This distillation of the metrics gives decision-makers a more comprehensible picture of their data so that their decisions don’t become either too subjective or disconnected from the data. Lower levels of metrics can be used underneath each of those top-level criteria to facilitate a more nuanced valuation. For example, if an organization needing to find good partner candidates scored and ranked (preferably in tiers) potential partners using decision criteria based on the characteristics of the most attractive partner, rather than just assuming that companies with the best reputation or biggest brands will be the best, then they will expeditiously identify the optimal partner candidates.

One of the reasons that companies have gotten so concerned with collecting and storing data rather than just making better decisions, Gordon believes, is that business decisions have become inherently more risky. The required size of investment is increasing in tandem with an increase in the time to return; time to return is a key determinant of risk. Data helps people feel like they are making competent decisions but in reality does little to reduce risk.

“If you’ve got lots of data, then the thinking is, ‘well, I did the best that I could because I got all of this data.’ People are worried that they might miss something,“ she said. “But that’s where I’m trying to come around and say, ‘yeah, but going and collecting more data, if you’ve got somebody like President Clinton, you’re just feeding into their tendency to put off making decisions. If you’ve got somebody like President Bush and you’re feeding into their tendency to ignore it, then there may be some really good information, good recommendations they’re ignoring.”

Gordon also says that having all the data possible to work with isn’t usually necessary—generally a representative sample will do. For example, she says the U.S Census Bureau takes the approach where it tries to count every citizen; consequently certain populations are chronically undercounted and inaccuracies pass undetected. The Canadian census, on the other hand, uses representative samples and thus tends to be much more accurate—and much less expensive to conduct. Organizations should also think about how they can find representative or “proxy” data in cases where collecting data that directly addresses a top-level decision criteria isn’t really practical.

To begin shifting the focus from collecting data inputs to improving decision outcomes, Gordon recommends clearly stating the decision objectives for each major decision and then identifying and defining the 3-5 criteria that are most important for achieving the decision objectives. She also recommends ensuring that there is sufficient governance and a process in place for making decisions including mechanisms for measuring the performance of the decision-making process and the outcomes resulting from the execution of that process. In addition, companies need to consider whether their decisions are made in a centralized or decentralized manner and then adapt decision governance accordingly.

One way that Enterprise Architects can help to encourage better decision-making within the organizations in which they work is to help in developing that governance rather than just providing data or data architectures, Gordon says. They should help stakeholders identify and define the important decision criteria, determine when full population rather than representative sampling is justified, recognize better methods for data analysis, and form decision recommendations based on that analysis. By gauging the appropriate blend of quantitative and qualitative data for a particular decision maker, an Architect can moderate gut types’ reliance on instinct and stimulate head and heart types’ intuition – thereby producing an optimally balanced decision. Architects should help lead and facilitate execution of the decision process, as well as help determine how data is presented within organizations in order to support the recommendations with the highest potential for meeting the decision objectives.

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penelopegordonPenelope Gordon recently led the expansion of the channel and service packaging strategies for Verizon’s cloud network products. Previously she was an IBM Strategist and Product Manager bringing emerging technologies such as predictive analytics to market. She helped to develop one of the world’s first public clouds.

 

 

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